Hemodynamic Monitoring during Veno-Venous Extracorporeal Membrane Oxygenation: A scoping review
Bibliographic record
Abstract
BackgroundIn adult patients receiving veno-venous Extracorporeal Membrane Oxygenation (VV ECMO), cardiovascular performance plays a critical role in determining oxygen delivery, organ perfusion and safe titration of extracorporeal support. Despite the increasing VV ECMO use, contemporary guidance on hemodynamic monitoring remains limited and largely experience-based. This scoping review aimed to map available basic and advanced monitoring approaches and to identify current evidence gaps.MethodsPubMed, EMBASE, and Cochrane CENTRAL were searched from inception until September 2025, along with reference lists of relevant articles. We included studies of any design reporting techniques, targets, or protocols for hemodynamic monitoring during VV ECMO.ResultsOf 465 records screened, 106 met inclusion criteria. No protocolized, evidence-based hemodynamic monitoring protocol specific to VV ECMO was identified. The available evidence was heterogeneous and mostly derived from physiologic studies or single-center observational cohorts. Findings were narratively synthesized across three domains: basic bedside monitoring, diagnostic/prognostic tools and advanced assessment of cardiopulmonary interaction. Across studies, no monitoring strategy consistently reduced time-to-wean or mortality. Observational data suggested that care bundles and multidisciplinary approaches may reduce complications. However, the risk of bias limits causal inference.ConclusionsDespite the complex interaction between native cardiovascular function and extracorporeal circulation, VV ECMO lacks consensus on evidence-based hemodynamic monitoring pathways. A pragmatic core monitoring bundle with tiered triggers for escalation is necessary. Future priorities include implementation models based on multidisciplinary teams, specific training, standardized bundles, and multicenter studies aimed to define right ventricular-centered targets to improve safety and clinical decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".